import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_mbart import MBartTokenizer
else:
				A						=			None


A						=			logging.get_logger(__name__)


A						=			{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

A						=			{
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

A						=			{
    'facebook/mbart-large-en-ro': 1_0_2_4,
    'facebook/mbart-large-cc25': 1_0_2_4,
}

# fmt: off
A						=			['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   VOCAB_FILES_NAMES
		lowerCAmelCase_     =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCAmelCase_     =   PRETRAINED_VOCAB_FILES_MAP
		lowerCAmelCase_     =   ['input_ids', 'attention_mask']
		lowerCAmelCase_     =   MBartTokenizer

		lowerCAmelCase_     =   []
		lowerCAmelCase_     =   []
		def __init__(       self     :     str     ,  snake_case__     :     Optional[Any]=None     ,  snake_case__     :     int=None     ,  snake_case__     :     Any="<s>"     ,  snake_case__     :     str="</s>"     ,  snake_case__     :     str="</s>"     ,  snake_case__     :     str="<s>"     ,  snake_case__     :     Tuple="<unk>"     ,  snake_case__     :     List[str]="<pad>"     ,  snake_case__     :     str="<mask>"     ,  snake_case__     :     Dict=None     ,  snake_case__     :     Dict=None     ,  snake_case__     :     Optional[int]=None     ,  **snake_case__     :     Any     ,  )      ->	Optional[Any]:
				# Mask token behave like a normal word, i.e. include the space before it
				_lowerCamelCase				   =  AddedToken(snake_case__     ,  lstrip=snake_case__     ,  rstrip=snake_case__		) if isinstance(snake_case__     ,  snake_case__		) else mask_token

				super().__init__(
				    vocab_file=snake_case__     ,  tokenizer_file=snake_case__     ,  bos_token=snake_case__     ,  eos_token=snake_case__     ,  sep_token=snake_case__     ,  cls_token=snake_case__     ,  unk_token=snake_case__     ,  pad_token=snake_case__     ,  mask_token=snake_case__     ,  src_lang=snake_case__     ,  tgt_lang=snake_case__     ,  additional_special_tokens=snake_case__     ,  **snake_case__     ,  )

				_lowerCamelCase				   =  vocab_file
				_lowerCamelCase				   =  False if not self.vocab_file else True

				_lowerCamelCase				   =  FAIRSEQ_LANGUAGE_CODES.copy()

				if additional_special_tokens is not None:
						# Only add those special tokens if they are not already there.
						_additional_special_tokens.extend(
						    [t for t in additional_special_tokens if t not in _additional_special_tokens]		)

				self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}		)
				_lowerCamelCase				   =  {
				    lang_code: self.convert_tokens_to_ids(snake_case__		) for lang_code in FAIRSEQ_LANGUAGE_CODES
				}

				_lowerCamelCase				   =  src_lang if src_lang is not None else 'en_XX'
				_lowerCamelCase				   =  self.convert_tokens_to_ids(self._src_lang		)
				_lowerCamelCase				   =  tgt_lang
				self.set_src_lang_special_tokens(self._src_lang		)
		@property
		def   _snake_case       (       self     :     Tuple		)      ->	str:
				return self._src_lang
		@src_lang.setter
		def   _snake_case       (       self     :     Optional[Any]     ,  snake_case__     :     str		)      ->	None:
				_lowerCamelCase				   =  new_src_lang
				self.set_src_lang_special_tokens(self._src_lang		)
		def   _snake_case       (       self     :     Tuple     ,  snake_case__     :     List[int]     ,  snake_case__     :     Optional[List[int]] = None		)      ->	List[int]:
				if token_ids_a is None:
						return self.prefix_tokens + token_ids_a + self.suffix_tokens
				# We don't expect to process pairs, but leave the pair logic for API consistency
				return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
		def   _snake_case       (       self     :     Optional[Any]     ,  snake_case__     :     List[int]     ,  snake_case__     :     Optional[List[int]] = None		)      ->	List[int]:
				_lowerCamelCase				   =  [self.sep_token_id]
				_lowerCamelCase				   =  [self.cls_token_id]

				if token_ids_a is None:
						return len(cls + token_ids_a + sep		) * [0]
				return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]
		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     str     ,  snake_case__     :     str     ,  snake_case__     :     Optional[str]     ,  snake_case__     :     Optional[str]     ,  **snake_case__     :     Tuple		)      ->	Tuple:
				if src_lang is None or tgt_lang is None:
						raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'		)
				_lowerCamelCase				   =  src_lang
				_lowerCamelCase				   =  self(snake_case__     ,  add_special_tokens=snake_case__     ,  return_tensors=snake_case__     ,  **snake_case__		)
				_lowerCamelCase				   =  self.convert_tokens_to_ids(snake_case__		)
				_lowerCamelCase				   =  tgt_lang_id
				return inputs
		def   _snake_case       (       self     :     str     ,  snake_case__     :     List[str]     ,  snake_case__     :     str = "en_XX"     ,  snake_case__     :     Optional[List[str]] = None     ,  snake_case__     :     str = "ro_RO"     ,  **snake_case__     :     Any     ,  )      ->	BatchEncoding:
				_lowerCamelCase				   =  src_lang
				_lowerCamelCase				   =  tgt_lang
				return super().prepare_seqaseq_batch(snake_case__     ,  snake_case__     ,  **snake_case__		)
		def   _snake_case       (       self     :     str		)      ->	Optional[Any]:
				return self.set_src_lang_special_tokens(self.src_lang		)
		def   _snake_case       (       self     :     str		)      ->	List[Any]:
				return self.set_tgt_lang_special_tokens(self.tgt_lang		)
		def   _snake_case       (       self     :     Tuple     ,  snake_case__     :     int		)      ->	None:
				_lowerCamelCase				   =  self.convert_tokens_to_ids(snake_case__		)
				_lowerCamelCase				   =  []
				_lowerCamelCase				   =  [self.eos_token_id, self.cur_lang_code]

				_lowerCamelCase				   =  self.convert_ids_to_tokens(self.prefix_tokens		)
				_lowerCamelCase				   =  self.convert_ids_to_tokens(self.suffix_tokens		)

				_lowerCamelCase				   =  processors.TemplateProcessing(
				    single=prefix_tokens_str + ['$A'] + suffix_tokens_str     ,  pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str     ,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str     ,  self.prefix_tokens + self.suffix_tokens		)		)     ,  )
		def   _snake_case       (       self     :     Optional[Any]     ,  snake_case__     :     str		)      ->	None:
				_lowerCamelCase				   =  self.convert_tokens_to_ids(snake_case__		)
				_lowerCamelCase				   =  []
				_lowerCamelCase				   =  [self.eos_token_id, self.cur_lang_code]

				_lowerCamelCase				   =  self.convert_ids_to_tokens(self.prefix_tokens		)
				_lowerCamelCase				   =  self.convert_ids_to_tokens(self.suffix_tokens		)

				_lowerCamelCase				   =  processors.TemplateProcessing(
				    single=prefix_tokens_str + ['$A'] + suffix_tokens_str     ,  pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str     ,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str     ,  self.prefix_tokens + self.suffix_tokens		)		)     ,  )



		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     str     ,  snake_case__     :     Optional[str] = None		)      ->	Tuple[str]:
				if not self.can_save_slow_tokenizer:
						raise ValueError(
						    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
						    'tokenizer.'		)

				if not os.path.isdir(snake_case__		):
						logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""		)
						return
				_lowerCamelCase				   =  os.path.join(
				    snake_case__     ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

				if os.path.abspath(self.vocab_file		) != os.path.abspath(snake_case__		):
						copyfile(self.vocab_file     ,  snake_case__		)

				return (out_vocab_file,)
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
A						=			logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')





@dataclass
class     lowerCAmelCase__     :


		'''simple docstring'''






		lowerCAmelCase_     =   field(
		    default='cifar10'     ,metadata={'help': 'Name of a dataset from the datasets package'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'The column name of the images in the files.'}			)
		lowerCAmelCase_     =   field(default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'A folder containing the training data.'}			)
		lowerCAmelCase_     =   field(default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'A folder containing the validation data.'}			)
		lowerCAmelCase_     =   field(
		    default=0.1_5     ,metadata={'help': 'Percent to split off of train for validation.'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of training examples to this '
		            'value if set.'
		        )
		    }     ,)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
		            'value if set.'
		        )
		    }     ,)
		def   _snake_case       (       self     :     Union[str, Any]		)      ->	Dict:
				_lowerCamelCase				   =  {}
				if self.train_dir is not None:
						_lowerCamelCase				   =  self.train_dir
				if self.validation_dir is not None:
						_lowerCamelCase				   =  self.validation_dir
				_lowerCamelCase				   =  data_files if data_files else None





@dataclass
class     lowerCAmelCase__     :


		'''simple docstring'''






		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
		        )
		    }     ,)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'Override some existing default config settings when a model is trained from scratch. Example: '
		            'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
		        )
		    }     ,)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}			)
		lowerCAmelCase_     =   field(
		    default='main'     ,metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}     ,)
		lowerCAmelCase_     =   field(default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'Name or path of preprocessor config.'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
		            'with private models).'
		        )
		    }     ,)
		lowerCAmelCase_     =   field(
		    default=0.7_5     ,metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'Whether or not to train with normalized pixel values as target.'}			)





@dataclass
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   field(
		    default=1e-3     ,metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}			)



def 	lowerCamelCase					(	UpperCamelCase     :				Union[str, Any]							)   ->    int:
		_lowerCamelCase				   =  torch.stack([example['pixel_values'] for example in examples]							)
		return {"pixel_values": pixel_values}


def 	lowerCamelCase					(	)   ->    Dict:
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		_lowerCamelCase				   =  HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)							)
		if len(sys.argv							) == 2 and sys.argv[1].endswith('.json'							):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
		else:
				_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  parser.parse_args_into_dataclasses()

		# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
		# information sent is the one passed as arguments along with your Python/PyTorch versions.
		send_example_telemetry('run_mae'			,       UpperCamelCase			,       UpperCamelCase							)

		# Setup logging
		logging.basicConfig(
		    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'			,       datefmt='%m/%d/%Y %H:%M:%S'			,       handlers=[logging.StreamHandler(sys.stdout							)]			,       )

		if training_args.should_log:
				# The default of training_args.log_level is passive, so we set log level at info here to have that default.
				transformers.utils.logging.set_verbosity_info()

		_lowerCamelCase				   =  training_args.get_process_log_level()
		logger.setLevel(UpperCamelCase							)
		transformers.utils.logging.set_verbosity(UpperCamelCase							)
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()

		# Log on each process the small summary:
		logger.warning(
		    F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
		    + F"""distributed training: {bool(training_args.local_rank != -1							)}, 16-bits training: {training_args.fpaa}"""							)
		logger.info(F"""Training/evaluation parameters {training_args}"""							)

		# Detecting last checkpoint.
		_lowerCamelCase				   =  None
		if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
				_lowerCamelCase				   =  get_last_checkpoint(training_args.output_dir							)
				if last_checkpoint is None and len(os.listdir(training_args.output_dir							)							) > 0:
						raise ValueError(
						    F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
						    'Use --overwrite_output_dir to overcome.'							)
				elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
						logger.info(
						    F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
						    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'							)

    # Initialize our dataset.
		_lowerCamelCase				   =  load_dataset(
		    data_args.dataset_name			,       data_args.dataset_config_name			,       data_files=data_args.data_files			,       cache_dir=model_args.cache_dir			,       use_auth_token=True if model_args.use_auth_token else None			,       )

		# If we don't have a validation split, split off a percentage of train as validation.
		_lowerCamelCase				   =  None if 'validation' in ds.keys() else data_args.train_val_split
		if isinstance(data_args.train_val_split			,       UpperCamelCase							) and data_args.train_val_split > 0.0:
				_lowerCamelCase				   =  ds['train'].train_test_split(data_args.train_val_split							)
				_lowerCamelCase				   =  split['train']
				_lowerCamelCase				   =  split['test']

		# Load pretrained model and image processor
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		_lowerCamelCase				   =  {
		    'cache_dir': model_args.cache_dir,
		    'revision': model_args.model_revision,
		    'use_auth_token': True if model_args.use_auth_token else None,
		}
		if model_args.config_name:
				_lowerCamelCase				   =  ViTMAEConfig.from_pretrained(model_args.config_name			,       **UpperCamelCase							)
		elif model_args.model_name_or_path:
				_lowerCamelCase				   =  ViTMAEConfig.from_pretrained(model_args.model_name_or_path			,       **UpperCamelCase							)
		else:
				_lowerCamelCase				   =  ViTMAEConfig()
				logger.warning('You are instantiating a new config instance from scratch.'							)
				if model_args.config_overrides is not None:
						logger.info(F"""Overriding config: {model_args.config_overrides}"""							)
						config.update_from_string(model_args.config_overrides							)
						logger.info(F"""New config: {config}"""							)

    # adapt config
		config.update(
		    {
		        'mask_ratio': model_args.mask_ratio,
		        'norm_pix_loss': model_args.norm_pix_loss,
		    }							)

		# create image processor
		if model_args.image_processor_name:
				_lowerCamelCase				   =  ViTImageProcessor.from_pretrained(model_args.image_processor_name			,       **UpperCamelCase							)
		elif model_args.model_name_or_path:
				_lowerCamelCase				   =  ViTImageProcessor.from_pretrained(model_args.model_name_or_path			,       **UpperCamelCase							)
		else:
				_lowerCamelCase				   =  ViTImageProcessor()

		# create model
		if model_args.model_name_or_path:
				_lowerCamelCase				   =  ViTMAEForPreTraining.from_pretrained(
				    model_args.model_name_or_path			,       from_tf=bool('.ckpt' in model_args.model_name_or_path							)			,       config=UpperCamelCase			,       cache_dir=model_args.cache_dir			,       revision=model_args.model_revision			,       use_auth_token=True if model_args.use_auth_token else None			,       )
		else:
				logger.info('Training new model from scratch'							)
				_lowerCamelCase				   =  ViTMAEForPreTraining(UpperCamelCase							)

		if training_args.do_train:
				_lowerCamelCase				   =  ds['train'].column_names
		else:
				_lowerCamelCase				   =  ds['validation'].column_names

		if data_args.image_column_name is not None:
				_lowerCamelCase				   =  data_args.image_column_name
		elif "image" in column_names:
				_lowerCamelCase				   =  'image'
		elif "img" in column_names:
				_lowerCamelCase				   =  'img'
		else:
				_lowerCamelCase				   =  column_names[0]

		# transformations as done in original MAE paper
		# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
		if "shortest_edge" in image_processor.size:
				_lowerCamelCase				   =  image_processor.size['shortest_edge']
		else:
				_lowerCamelCase				   =  (image_processor.size['height'], image_processor.size['width'])
		_lowerCamelCase				   =  Compose(
		    [
		        Lambda(lambda UpperCamelCase							: img.convert('RGB'							) if img.mode != "RGB" else img							),
		        RandomResizedCrop(UpperCamelCase			,       scale=(0.2, 1.0)			,       interpolation=InterpolationMode.BICUBIC							),
		        RandomHorizontalFlip(),
		        ToTensor(),
		        Normalize(mean=image_processor.image_mean			,       std=image_processor.image_std							),
		    ]							)

		def preprocess_images(UpperCamelCase     :				Tuple							):
				_lowerCamelCase				   =  [transforms(UpperCamelCase							) for image in examples[image_column_name]]
				return examples

		if training_args.do_train:
				if "train" not in ds:
						raise ValueError('--do_train requires a train dataset'							)
				if data_args.max_train_samples is not None:
						_lowerCamelCase				   =  ds['train'].shuffle(seed=training_args.seed							).select(range(data_args.max_train_samples							)							)
				# Set the training transforms
				ds["train"].set_transform(UpperCamelCase							)

		if training_args.do_eval:
				if "validation" not in ds:
						raise ValueError('--do_eval requires a validation dataset'							)
				if data_args.max_eval_samples is not None:
						_lowerCamelCase				   =  (
						    ds['validation'].shuffle(seed=training_args.seed							).select(range(data_args.max_eval_samples							)							)
						)
				# Set the validation transforms
				ds["validation"].set_transform(UpperCamelCase							)

		# Compute absolute learning rate
		_lowerCamelCase				   =  (
		    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
		)
		if training_args.base_learning_rate is not None:
				_lowerCamelCase				   =  training_args.base_learning_rate * total_train_batch_size / 2_56

		# Initialize our trainer
		_lowerCamelCase				   =  Trainer(
		    model=UpperCamelCase			,       args=UpperCamelCase			,       train_dataset=ds['train'] if training_args.do_train else None			,       eval_dataset=ds['validation'] if training_args.do_eval else None			,       tokenizer=UpperCamelCase			,       data_collator=UpperCamelCase			,       )

		# Training
		if training_args.do_train:
				_lowerCamelCase				   =  None
				if training_args.resume_from_checkpoint is not None:
						_lowerCamelCase				   =  training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						_lowerCamelCase				   =  last_checkpoint
				_lowerCamelCase				   =  trainer.train(resume_from_checkpoint=UpperCamelCase							)
				trainer.save_model()
				trainer.log_metrics('train'			,       train_result.metrics							)
				trainer.save_metrics('train'			,       train_result.metrics							)
				trainer.save_state()

		# Evaluation
		if training_args.do_eval:
				_lowerCamelCase				   =  trainer.evaluate()
				trainer.log_metrics('eval'			,       UpperCamelCase							)
				trainer.save_metrics('eval'			,       UpperCamelCase							)

		# Write model card and (optionally) push to hub
		_lowerCamelCase				   =  {
		    'tasks': 'masked-auto-encoding',
		    'dataset': data_args.dataset_name,
		    'tags': ['masked-auto-encoding'],
		}
		if training_args.push_to_hub:
				trainer.push_to_hub(**UpperCamelCase							)
		else:
				trainer.create_model_card(**UpperCamelCase							)


def 	lowerCamelCase					(	UpperCamelCase     :				Any							)   ->    Dict:
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
				main()